16psk

在Matlab环境中，使用高阶有限差分算法实现双相介质地震波场正演模拟(Forward modeling for two-phase media)

模式识别与智能计算（杨淑莹）大量源码，不容错过(Pattern Recognition and Intelligent Computing lot Source)

说明：  dpd的matlab程序5个，需要配套使用，包含saleh模型，多项式模型和volterra模型(dpd using matlab, Including Saleh model, polynomial model and Volterra model)

This is Kalman Filter Tool box.

function varargout = pptemp(varargin) PPTEMP M-file for pptemp.fig

voice tool for voice recognition

在MATLAB中实现地球两点的的计算,可以方便的得出方位,仰角,距离(in MATLAB 2:00 of the Earth's calculation, can easily come to the position, elevation, distance)

基于滑模变结构自抗扰的PMSM 速度控制系统(Variable structure ADRC PMSM speed control system based on sliding mode)

bus system data for matlab code

在机动目标跟踪中，机动目标模型是机动目标跟踪的基本要素之一，一般希望机动目标模型能准确表征目标机动时的各种运动状态。比较常用的模型有匀速运动(CV)模型、匀加速运动(CA) 模型、时间相关模型（Singer）和机动目标“当前”统计模型。上述模型均采用机动频率表征目标的机动情况。在应用当中，通常采用固定的机动频率，这就表示机动目标的机动时间是一定的，而实际上机动目标的机动时间是不断变化的，也就是说机动频率是不断变化的，采用固定机动频率必然会带来误差。采样周期在0.5—2S时，机动频率越小跟踪精度越高[1]，但机动频率仍然是固定值。本文提出的基于神经网络的机动频率自适应调整方法可以使机动频率随机动而变化，从而提高状态估计的准确性，提高跟踪精度。本文将小波神经网络用于机动目标跟踪中机动频率的自适应调整，该算法对机动目标“当前”统计模型中的机动频率进行实时修改, 从而自适应的改变机动频率,使跟踪算法与目标的真实状态更接近。该算法采用小波神经网络的离线训练，实时性好。(The maneuver of the maneuvering target is uncertain. The maneuvering frequency is constantly changeable, but traditionally it is beforehand determined as a constant based on the target state estimation in the state model of the maneuvering target. The maneuver of the maneuvering target makes the kinematics equation of the target model mismatch with the practical motion model and the tracking error will be increased. Based on the advantages of the self-learning, the rapid convergence rate and the nonlinear approximation ability of the wavelet neural network, it was put forward to be used in the field of target tracking in the paper. The new residual is used as the input of the wavelet neural network, the output of the network is used to adjust adaptively the maneuvering frequency of the CS model. The algorithm is more close to the real state of the target. The simulation results showed that tracking error can be reduced and the tracking accuracy can be improved.)